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  • Perspective
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What neuroscience can tell AI about learning in continuously changing environments

Nature Machine Intelligence volume 7, pages 1897–1912 (2025)Cite this article

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Abstract

Modern artificial intelligence (AI) models, such as large language models, are usually trained once on a huge corpus of data, potentially fine-tuned for a specific task and then deployed with fixed parameters. Their training is costly, slow and gradual, requiring billions of repetitions. In stark contrast, animals continuously adapt to the ever-changing contingencies in their environments. This is particularly important for social species, where behavioural policies and reward outcomes may frequently change in interaction with peers. The underlying computational processes are often marked by rapid shifts in an animal’s behaviour and rather sudden transitions in neuronal population activity. Such computational capacities are of growing importance for AI systems operating in the real world, like those guiding robots or autonomous vehicles, or for agentic AI interacting with humans online. Can AI learn from neuroscience? This Perspective explores this question, integrating the literature on continual and in-context learning in AI with the neuroscience of learning on behavioural tasks with shifting rules, reward probabilities or outcomes. We outline an agenda for how the links between neuroscience and AI could be tightened, thus supporting the transfer of ideas and findings between both areas and contributing to the evolving field of NeuroAI.

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Fig. 1: Continual and in-context learning.
Fig. 2: Sudden transitions in behaviour and neural population representations during rule learning.
Fig. 3: Dynamical mechanisms and their experimental support.
Fig. 4: Fast plasticity mechanisms supporting episodic memory.
Fig. 5: Learning computational surrogate models from neuronal and behavioural data.

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Acknowledgements

D.D. appreciates funding from the German Science Foundation (DFG) via grants Du 354/14-1 (within the research cluster FOR-5159 dedicated to prefrontal flexibility), Du 354/15-1 and Du 354/18-1. B.A. was supported by the Intramural Research Program of the NIMH (ZIA MH002928-01). G.K. acknowledges funding from the Hector II foundation. We thank M. Kaden for her help with the figures and references.

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Authors and Affiliations

  1. Department of Theoretical Neuroscience, Central Institute of Mental Health, Mannheim, Germany

    Daniel Durstewitz

  2. Interdisciplinary Center for Scientific Computing, Heidelberg University, Heidelberg, Germany

    Daniel Durstewitz & Georgia Koppe

  3. Faculty of Physics and Astronomy, Heidelberg University, Heidelberg, Germany

    Daniel Durstewitz

  4. Section on Learning and Decision Making, National Institute of Mental Health, Bethesda, MD, USA

    Bruno Averbeck

  5. Department of Psychiatry and Psychotherapy & Hector Institute for AI in Psychiatry, Central Institute of Mental Health, Mannheim, Germany

    Georgia Koppe

  6. Hertie Institute for AI in Brain Health, University of Tübingen, Tübingen, Germany

    Georgia Koppe

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Durstewitz, D., Averbeck, B. & Koppe, G. What neuroscience can tell AI about learning in continuously changing environments. Nat Mach Intell 7, 1897–1912 (2025). https://doi.org/10.1038/s42256-025-01146-z

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